{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "demo_MSSVConv.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyPJ6ObuVKAM1gy8IDDfJzeA",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/humanpose1/MS-SVConv/blob/main/notebook/demo_MSSVConv.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "slKL_LEw6P3c"
      },
      "source": [
        "# Demo of MS SVConv for point cloud registration\n",
        "\n",
        "The goal of this notebook is too quickly show how you can use MS-SVConv on your projects.\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fv1Ca0XF6pHz"
      },
      "source": [
        "## Installation\n",
        "First we install necessary packages"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vbPbVET4Bv15",
        "outputId": "d1ca0808-0166-439b-d4f5-fd447ea30acf"
      },
      "source": [
        "!pip install torch torchvision torchaudio ninja\n",
        "!pip uninstall -y torch-scatter\n",
        "!pip uninstall -y torch-sparse\n",
        "!pip uninstall -y torch-cluster\n",
        "!pip uninstall -y torch-geometric\n",
        "!pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.8.0+cu101.html\n",
        "!pip install torch-sparse -f https://pytorch-geometric.com/whl/torch-1.8.0+cu101.html\n",
        "!pip install torch-cluster -f https://pytorch-geometric.com/whl/torch-1.8.0+cu101.html\n",
        "!pip install torch-spline-conv -f https://pytorch-geometric.com/whl/torch-1.8.0+cu101.html\n",
        "!pip install torch-geometric\n",
        "!pip install pyvista\n",
        "!pip install --upgrade jsonschema\n",
        "\n",
        "!pip install git+https://github.com/nicolas-chaulet/torch-points3d.git\n",
        "!apt-get update\n",
        "!apt-get install -qq xvfb libgl1-mesa-glx"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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            "Installing collected packages: torchaudio, ninja\n",
            "Successfully installed ninja-1.10.0.post2 torchaudio-0.8.1\n",
            "\u001b[33mWARNING: Skipping torch-scatter as it is not installed.\u001b[0m\n",
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            "Building wheels for collected packages: torch-geometric\n",
            "  Building wheel for torch-geometric (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for torch-geometric: filename=torch_geometric-1.7.0-cp37-none-any.whl size=365386 sha256=df0460fb5148376097eddd7893b97008ce12543758a5d93712bf0888053c293a\n",
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            "Successfully built torch-geometric\n",
            "Installing collected packages: isodate, rdflib, ase, torch-geometric\n",
            "Successfully installed ase-3.21.1 isodate-0.6.0 rdflib-5.0.0 torch-geometric-1.7.0\n",
            "Collecting pyvista\n",
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            "Building wheels for collected packages: transforms3d\n",
            "  Building wheel for transforms3d (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for transforms3d: filename=transforms3d-0.3.1-cp37-none-any.whl size=59374 sha256=d8d66a99d0ed887f91beabdbc654393d39bcf2be39b33658c896c71f3d432a6d\n",
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            "Successfully built transforms3d\n",
            "Installing collected packages: meshio, vtk, scooby, transforms3d, pyvista\n",
            "Successfully installed meshio-4.3.12 pyvista-0.29.1 scooby-0.5.7 transforms3d-0.3.1 vtk-9.0.1\n",
            "Collecting jsonschema\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/c5/8f/51e89ce52a085483359217bc72cdbf6e75ee595d5b1d4b5ade40c7e018b8/jsonschema-3.2.0-py2.py3-none-any.whl (56kB)\n",
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            "\u001b[31mERROR: nbclient 0.5.3 has requirement jupyter-client>=6.1.5, but you'll have jupyter-client 5.3.5 which is incompatible.\u001b[0m\n",
            "Installing collected packages: jsonschema\n",
            "  Found existing installation: jsonschema 2.6.0\n",
            "    Uninstalling jsonschema-2.6.0:\n",
            "      Successfully uninstalled jsonschema-2.6.0\n",
            "Successfully installed jsonschema-3.2.0\n",
            "Collecting git+https://github.com/nicolas-chaulet/torch-points3d.git\n",
            "  Cloning https://github.com/nicolas-chaulet/torch-points3d.git to /tmp/pip-req-build-t5bhol2u\n",
            "  Running command git clone -q https://github.com/nicolas-chaulet/torch-points3d.git /tmp/pip-req-build-t5bhol2u\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
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            "  Downloading https://files.pythonhosted.org/packages/b7/b2/d7f70a85d3f6b0365517782632f150e3bbc2fb8e998cd69e27deba599aae/torchnet-0.0.4.tar.gz\n",
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            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/3e/ba/8e6c380f3b7b34e6dafe2392f5c79275f2245588a7dc0b690fc344491eed/pytorch_metric_learning-0.9.98-py3-none-any.whl (102kB)\n",
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            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
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            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/04/be/8c88cee3366de2a3a23a9ff1a8be34e79ad1eb1ceb0d0e33aca83655ac3c/numba-0.50.1-cp37-cp37m-manylinux2014_x86_64.whl (3.6MB)\n",
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            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/c9/75/e078f5a2e1df7e0d3044749089fc2823e62d029cc027ed8ae5d71fafcbdc/visdom-0.1.8.9.tar.gz (676kB)\n",
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            "  Downloading https://files.pythonhosted.org/packages/2f/53/d285f1faa36eca666f6d04ac17c5f3a9e72cedaeb530e663fdc025209780/omegaconf-1.4.1-py3-none-any.whl\n",
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            "Collecting jsonpointer>=1.9\n",
            "  Downloading https://files.pythonhosted.org/packages/23/52/05f67532aa922e494c351344e0d9624a01f74f5dd8402fe0d1b563a6e6fc/jsonpointer-2.1-py2.py3-none-any.whl\n",
            "Collecting smmap<5,>=3.0.1\n",
            "  Downloading https://files.pythonhosted.org/packages/68/ee/d540eb5e5996eb81c26ceffac6ee49041d473bc5125f2aa995cf51ec1cf1/smmap-4.0.0-py2.py3-none-any.whl\n",
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            "Requirement already satisfied: bleach in /usr/local/lib/python3.7/dist-packages (from nbconvert->notebook->open3d==0.10.0.0->torch-points3d==0.2.0) (3.3.0)\n",
            "Requirement already satisfied: testpath in /usr/local/lib/python3.7/dist-packages (from nbconvert->notebook->open3d==0.10.0.0->torch-points3d==0.2.0) (0.4.4)\n",
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            "Requirement already satisfied: ptyprocess; os_name != \"nt\" in /usr/local/lib/python3.7/dist-packages (from terminado>=0.8.1->notebook->open3d==0.10.0.0->torch-points3d==0.2.0) (0.7.0)\n",
            "Requirement already satisfied: pickleshare in /usr/local/lib/python3.7/dist-packages (from ipython>=4.0.0; python_version >= \"3.3\"->ipywidgets->open3d==0.10.0.0->torch-points3d==0.2.0) (0.7.5)\n",
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            "Requirement already satisfied: webencodings in /usr/local/lib/python3.7/dist-packages (from bleach->nbconvert->notebook->open3d==0.10.0.0->torch-points3d==0.2.0) (0.5.1)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from bleach->nbconvert->notebook->open3d==0.10.0.0->torch-points3d==0.2.0) (20.9)\n",
            "Requirement already satisfied: wcwidth in /usr/local/lib/python3.7/dist-packages (from prompt-toolkit<2.0.0,>=1.0.4->ipython>=4.0.0; python_version >= \"3.3\"->ipywidgets->open3d==0.10.0.0->torch-points3d==0.2.0) (0.2.5)\n",
            "Building wheels for collected packages: torch-points3d, gdown\n",
            "  Building wheel for torch-points3d (PEP 517) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for torch-points3d: filename=torch_points3d-0.2.0-cp37-none-any.whl size=349406 sha256=0191bed181009b5aefa2cf4131dae7071f7137fdad81364c21c5152b0ef7c3da\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-0gfhba5j/wheels/1a/1d/ba/bffa6ba5682091b0c181a92a8d56fdb106c460a11c368cbc32\n",
            "  Building wheel for gdown (PEP 517) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for gdown: filename=gdown-3.12.2-cp37-none-any.whl size=9693 sha256=0fcc78bd4235f3ff24edf64d9c84e6fb18f576b33849fedab33b0be02fbc4881\n",
            "  Stored in directory: /root/.cache/pip/wheels/81/d0/d7/d9983facc6f2775411803e0e2d30ebf98efbf2fc6e57701e09\n",
            "Successfully built torch-points3d gdown\n",
            "Building wheels for collected packages: torchnet, torch-points-kernels, visdom, subprocess32, gql, torchfile, graphql-core\n",
            "  Building wheel for torchnet (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for torchnet: filename=torchnet-0.0.4-cp37-none-any.whl size=29743 sha256=eaf2011a738d2e9adab81b9f4c1dde91f5f0d6c98d73f55c37620300a6be1dde\n",
            "  Stored in directory: /root/.cache/pip/wheels/e1/03/fb/1c212c2f20905cdf97fe39022946cf16b8e66ed754a6663400\n",
            "  Building wheel for torch-points-kernels (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for torch-points-kernels: filename=torch_points_kernels-0.6.10-cp37-cp37m-linux_x86_64.whl size=14277135 sha256=63de1327b21a5ee9ee4c4f9841a92465c8015db4f0489de3d71c4a8706c4f6af\n",
            "  Stored in directory: /root/.cache/pip/wheels/9a/bf/57/1399012267706820e9cf1f8f290b6960dd1a21706016a67d11\n",
            "  Building wheel for visdom (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for visdom: filename=visdom-0.1.8.9-cp37-none-any.whl size=655251 sha256=0f383530c43fc0138cdb64e9f256eed9276a35a9ac10a3978c738fb1314c9df6\n",
            "  Stored in directory: /root/.cache/pip/wheels/70/19/a7/6d589ed967f4dfefd33bc166d081257bd4ed0cb618dccfd62a\n",
            "  Building wheel for subprocess32 (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for subprocess32: filename=subprocess32-3.5.4-cp37-none-any.whl size=6489 sha256=72f74d3ad05117b82da383c550cafb9cc811387f671becd5c8569bb20b41b861\n",
            "  Stored in directory: /root/.cache/pip/wheels/68/39/1a/5e402bdfdf004af1786c8b853fd92f8c4a04f22aad179654d1\n",
            "  Building wheel for gql (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for gql: filename=gql-0.2.0-cp37-none-any.whl size=7629 sha256=8d22fcc321dc755f4b1c3954a28d5dab661d64b60105eac167bbe76f73b4c86e\n",
            "  Stored in directory: /root/.cache/pip/wheels/ce/0e/7b/58a8a5268655b3ad74feef5aa97946f0addafb3cbb6bd2da23\n",
            "  Building wheel for torchfile (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for torchfile: filename=torchfile-0.1.0-cp37-none-any.whl size=5713 sha256=9eb10223f54ce902904dd1adf8135c1f84ed4ad0ec1c3575d0b6c26ea0a50f3e\n",
            "  Stored in directory: /root/.cache/pip/wheels/b1/c3/d6/9a1cc8f3a99a0fc1124cae20153f36af59a6e683daca0a0814\n",
            "  Building wheel for graphql-core (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for graphql-core: filename=graphql_core-1.1-cp37-none-any.whl size=104651 sha256=983636c4a7ec77287fc911e8c450cf07b8988e8ab403e5d8db6c7e81b418d952\n",
            "  Stored in directory: /root/.cache/pip/wheels/45/99/d7/c424029bb0fe910c63b68dbf2aa20d3283d023042521bcd7d5\n",
            "Successfully built torchnet torch-points-kernels visdom subprocess32 gql torchfile graphql-core\n",
            "Installing collected packages: jsonpointer, jsonpatch, torchfile, websocket-client, visdom, torchnet, omegaconf, hydra-core, plyfile, watchdog, shortuuid, docker-pycreds, subprocess32, sentry-sdk, configparser, smmap, gitdb, GitPython, graphql-core, gql, wandb, pytorch-metric-learning, gdown, llvmlite, numba, torch-points-kernels, open3d, torch-points3d\n",
            "  Found existing installation: gdown 3.6.4\n",
            "    Uninstalling gdown-3.6.4:\n",
            "      Successfully uninstalled gdown-3.6.4\n",
            "  Found existing installation: llvmlite 0.34.0\n",
            "    Uninstalling llvmlite-0.34.0:\n",
            "      Successfully uninstalled llvmlite-0.34.0\n",
            "  Found existing installation: numba 0.51.2\n",
            "    Uninstalling numba-0.51.2:\n",
            "      Successfully uninstalled numba-0.51.2\n",
            "Successfully installed GitPython-3.1.14 configparser-5.0.2 docker-pycreds-0.4.0 gdown-3.12.2 gitdb-4.0.7 gql-0.2.0 graphql-core-1.1 hydra-core-0.11.3 jsonpatch-1.32 jsonpointer-2.1 llvmlite-0.33.0 numba-0.50.1 omegaconf-1.4.1 open3d-0.10.0.0 plyfile-0.7.3 pytorch-metric-learning-0.9.98 sentry-sdk-1.0.0 shortuuid-1.0.1 smmap-4.0.0 subprocess32-3.5.4 torch-points-kernels-0.6.10 torch-points3d-0.2.0 torchfile-0.1.0 torchnet-0.0.4 visdom-0.1.8.9 wandb-0.8.36 watchdog-2.0.2 websocket-client-0.58.0\n",
            "Selecting previously unselected package libgl1-mesa-glx:amd64.\n",
            "(Reading database ... 160983 files and directories currently installed.)\n",
            "Preparing to unpack .../libgl1-mesa-glx_20.0.8-0ubuntu1~18.04.1_amd64.deb ...\n",
            "Unpacking libgl1-mesa-glx:amd64 (20.0.8-0ubuntu1~18.04.1) ...\n",
            "Selecting previously unselected package xvfb.\n",
            "Preparing to unpack .../xvfb_2%3a1.19.6-1ubuntu4.8_amd64.deb ...\n",
            "Unpacking xvfb (2:1.19.6-1ubuntu4.8) ...\n",
            "Setting up xvfb (2:1.19.6-1ubuntu4.8) ...\n",
            "Setting up libgl1-mesa-glx:amd64 (20.0.8-0ubuntu1~18.04.1) ...\n",
            "Processing triggers for man-db (2.8.3-2ubuntu0.1) ...\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7U_MRvqiBg__",
        "outputId": "adaad25c-1c60-447c-b8ef-1513025eff91"
      },
      "source": [
        "!pip install pyvistaqt\n",
        "!apt-get install build-essential python3-dev libopenblas-dev\n",
        "!pip install -U git+https://github.com/NVIDIA/MinkowskiEngine --no-deps\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting pyvistaqt\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/4d/b6/486546401d4f185dd8bc73dec8f6a62411b1ae954afbb6d0f07c4b1baf3a/pyvistaqt-0.3.0-py3-none-any.whl (160kB)\n",
            "\u001b[K     |████████████████████████████████| 163kB 10.8MB/s \n",
            "\u001b[?25hRequirement already satisfied: pyvista>=0.25.0 in /usr/local/lib/python3.7/dist-packages (from pyvistaqt) (0.29.1)\n",
            "Requirement already satisfied: QtPy>=1.9.0 in /usr/local/lib/python3.7/dist-packages (from pyvistaqt) (1.9.0)\n",
            "Collecting PyQt5>=5.11.3imageio>=2.5.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/ed/62/cd9f10702c75b242f82da858668fba0cda04cda92133244d3d1555e530b4/PyQt5-5.15.4-cp36.cp37.cp38.cp39-abi3-manylinux2014_x86_64.whl (8.3MB)\n",
            "\u001b[K     |████████████████████████████████| 8.3MB 10.5MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from pyvista>=0.25.0->pyvistaqt) (1.19.5)\n",
            "Requirement already satisfied: imageio in /usr/local/lib/python3.7/dist-packages (from pyvista>=0.25.0->pyvistaqt) (2.4.1)\n",
            "Requirement already satisfied: transforms3d==0.3.1 in /usr/local/lib/python3.7/dist-packages (from pyvista>=0.25.0->pyvistaqt) (0.3.1)\n",
            "Requirement already satisfied: meshio<5.0,>=4.0.3 in /usr/local/lib/python3.7/dist-packages (from pyvista>=0.25.0->pyvistaqt) (4.3.12)\n",
            "Requirement already satisfied: pillow in /usr/local/lib/python3.7/dist-packages (from pyvista>=0.25.0->pyvistaqt) (7.1.2)\n",
            "Requirement already satisfied: scooby>=0.5.1 in /usr/local/lib/python3.7/dist-packages (from pyvista>=0.25.0->pyvistaqt) (0.5.7)\n",
            "Requirement already satisfied: vtk in /usr/local/lib/python3.7/dist-packages (from pyvista>=0.25.0->pyvistaqt) (9.0.1)\n",
            "Requirement already satisfied: appdirs in /usr/local/lib/python3.7/dist-packages (from pyvista>=0.25.0->pyvistaqt) (1.4.4)\n",
            "Collecting PyQt5-Qt5>=5.15\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/83/d4/241a6a518d0bcf0a9fcdcbad5edfed18d43e884317eab8d5230a2b27e206/PyQt5_Qt5-5.15.2-py3-none-manylinux2014_x86_64.whl (59.9MB)\n",
            "\u001b[K     |████████████████████████████████| 59.9MB 1.3MB/s \n",
            "\u001b[?25hCollecting PyQt5-sip<13,>=12.8\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/31/24/f887203677955ba4d5d4efe9176ac7ed2bf84efce8c243ab91e63183ad9e/PyQt5_sip-12.8.1-cp37-cp37m-manylinux1_x86_64.whl (283kB)\n",
            "\u001b[K     |████████████████████████████████| 286kB 38.0MB/s \n",
            "\u001b[?25hRequirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from meshio<5.0,>=4.0.3->pyvista>=0.25.0->pyvistaqt) (3.10.0)\n",
            "Requirement already satisfied: typing-extensions>=3.6.4; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->meshio<5.0,>=4.0.3->pyvista>=0.25.0->pyvistaqt) (3.7.4.3)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->meshio<5.0,>=4.0.3->pyvista>=0.25.0->pyvistaqt) (3.4.1)\n",
            "Installing collected packages: PyQt5-Qt5, PyQt5-sip, PyQt5, pyvistaqt\n",
            "Successfully installed PyQt5-5.15.4 PyQt5-Qt5-5.15.2 PyQt5-sip-12.8.1 pyvistaqt-0.3.0\n",
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "build-essential is already the newest version (12.4ubuntu1).\n",
            "libopenblas-dev is already the newest version (0.2.20+ds-4).\n",
            "python3-dev is already the newest version (3.6.7-1~18.04).\n",
            "python3-dev set to manually installed.\n",
            "0 upgraded, 0 newly installed, 0 to remove and 31 not upgraded.\n",
            "Collecting git+https://github.com/NVIDIA/MinkowskiEngine\n",
            "  Cloning https://github.com/NVIDIA/MinkowskiEngine to /tmp/pip-req-build-6m1k86t1\n",
            "  Running command git clone -q https://github.com/NVIDIA/MinkowskiEngine /tmp/pip-req-build-6m1k86t1\n",
            "Building wheels for collected packages: MinkowskiEngine\n",
            "  Building wheel for MinkowskiEngine (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for MinkowskiEngine: filename=MinkowskiEngine-0.5.3-cp37-cp37m-linux_x86_64.whl size=5235315 sha256=e3e5cfa9c6f13d0188c7691e686496111464cb4dbd59b30f009eb439d4002eb9\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-hvv7kx9s/wheels/6e/79/a9/7f7fa333217e5c5836b350d13bec2ddaa3c6839aeb2b76b665\n",
            "Successfully built MinkowskiEngine\n",
            "Installing collected packages: MinkowskiEngine\n",
            "Successfully installed MinkowskiEngine-0.5.3\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0upTEPRr_ofL"
      },
      "source": [
        "We install also install torchsparse"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x1Bc0Ait_oc1"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_fSreyeXWU1E",
        "outputId": "6e8768d0-664a-4125-e258-c724efc20fd5"
      },
      "source": [
        "!apt-get install libsparsehash-dev\n",
        "!pip install --upgrade git+https://github.com/mit-han-lab/torchsparse.git"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "The following NEW packages will be installed:\n",
            "  libsparsehash-dev\n",
            "0 upgraded, 1 newly installed, 0 to remove and 31 not upgraded.\n",
            "Need to get 72.4 kB of archives.\n",
            "After this operation, 612 kB of additional disk space will be used.\n",
            "Get:1 http://archive.ubuntu.com/ubuntu bionic/universe amd64 libsparsehash-dev all 2.0.2-1 [72.4 kB]\n",
            "Fetched 72.4 kB in 0s (1,227 kB/s)\n",
            "Selecting previously unselected package libsparsehash-dev.\n",
            "(Reading database ... 160996 files and directories currently installed.)\n",
            "Preparing to unpack .../libsparsehash-dev_2.0.2-1_all.deb ...\n",
            "Unpacking libsparsehash-dev (2.0.2-1) ...\n",
            "Setting up libsparsehash-dev (2.0.2-1) ...\n",
            "Collecting git+https://github.com/mit-han-lab/torchsparse.git\n",
            "  Cloning https://github.com/mit-han-lab/torchsparse.git to /tmp/pip-req-build-0j9ixhai\n",
            "  Running command git clone -q https://github.com/mit-han-lab/torchsparse.git /tmp/pip-req-build-0j9ixhai\n",
            "Building wheels for collected packages: torchsparse\n",
            "  Building wheel for torchsparse (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for torchsparse: filename=torchsparse-1.2.0-cp37-cp37m-linux_x86_64.whl size=14920910 sha256=9440e6e6d02277e3e60870debd4efde0715de338acd1b54263646df02682744b\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-5lrswyal/wheels/72/49/74/b7f6b675547934724b2eaa6be758875019f1a0eaf821a99162\n",
            "Successfully built torchsparse\n",
            "Installing collected packages: torchsparse\n",
            "Successfully installed torchsparse-1.2.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l0q4WUP1_xL2"
      },
      "source": [
        "## Registration\n",
        "\n",
        "We will use pyvista for the visualization. But it is not interactive..."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NPVXHmzQMu_R",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "14e2cfe5-c7d0-4249-a445-75ab94cda21c"
      },
      "source": [
        "import numpy as np\n",
        "import open3d as o3d\n",
        "import os\n",
        "import os.path as osp\n",
        "import pathlib\n",
        "import requests\n",
        "import torch\n",
        "from torch_points3d.applications.pretrained_api import PretainedRegistry\n",
        "from torch_points3d.core.data_transform import GridSampling3D, AddFeatByKey, AddOnes, Random3AxisRotation\n",
        "from torch_points3d.datasets.registration.pair import Pair\n",
        "from torch_points3d.utils.registration import get_matches, fast_global_registration\n",
        "from torch_geometric.data import Data\n",
        "from torch_geometric.transforms import Compose\n",
        "from torch_points3d.metrics.registration_metrics import compute_hit_ratio\n",
        "from zipfile import ZipFile\n",
        "\n",
        "import pyvista as pv\n",
        "import pyvistaqt as pvqt\n",
        "import panel as pn\n",
        "\n",
        "pn.extension('vtk')\n",
        "os.system('/usr/bin/Xvfb :99 -screen 0 1024x768x24 &')\n",
        "os.environ['DISPLAY'] = ':99'\n",
        "os.environ['PYVISTA_OFF_SCREEN'] = 'True'\n",
        "os.environ['PYVISTA_USE_PANEL'] = 'True'"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "(function(root) {\n",
              "  function now() {\n",
              "    return new Date();\n",
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              "\n",
              "  var force = true;\n",
              "\n",
              "  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n",
              "    root._bokeh_onload_callbacks = [];\n",
              "    root._bokeh_is_loading = undefined;\n",
              "  }\n",
              "\n",
              "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
              "    root._bokeh_timeout = Date.now() + 5000;\n",
              "    root._bokeh_failed_load = false;\n",
              "  }\n",
              "\n",
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              "      root._bokeh_onload_callbacks.forEach(function(callback) {\n",
              "        if (callback != null)\n",
              "          callback();\n",
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              "    } finally {\n",
              "      delete root._bokeh_onload_callbacks\n",
              "    }\n",
              "    console.debug(\"Bokeh: all callbacks have finished\");\n",
              "  }\n",
              "\n",
              "  function load_libs(css_urls, js_urls, js_modules, callback) {\n",
              "    if (css_urls == null) css_urls = [];\n",
              "    if (js_urls == null) js_urls = [];\n",
              "    if (js_modules == null) js_modules = [];\n",
              "\n",
              "    root._bokeh_onload_callbacks.push(callback);\n",
              "    if (root._bokeh_is_loading > 0) {\n",
              "      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
              "      return null;\n",
              "    }\n",
              "    if (js_urls.length === 0 && js_modules.length === 0) {\n",
              "      run_callbacks();\n",
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              "    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
              "    root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length;\n",
              "\n",
              "    function on_load() {\n",
              "      root._bokeh_is_loading--;\n",
              "      if (root._bokeh_is_loading === 0) {\n",
              "        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n",
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              "\n",
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              "\n",
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              "      const element = document.createElement(\"link\");\n",
              "      element.onload = on_load;\n",
              "      element.onerror = on_error;\n",
              "      element.rel = \"stylesheet\";\n",
              "      element.type = \"text/css\";\n",
              "      element.href = url;\n",
              "      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
              "      document.body.appendChild(element);\n",
              "    }\n",
              "\n",
              "    var skip = [];\n",
              "    if (window.requirejs) {\n",
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              "      var urls = ['https://unpkg.com/tabulator-tables@4.9.3/dist/js/tabulator.js', 'https://unpkg.com/moment@2.27.0/moment.js'];\n",
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              "        skip.push(urls[i])\n",
              "      }\n",
              "    }\n",
              "    if (((window['vtk'] !== undefined) && (!(window['vtk'] instanceof HTMLElement))) || window.requirejs) {\n",
              "      var urls = ['https://unpkg.com/vtk.js@14.16.4/dist/vtk.js'];\n",
              "      for (var i = 0; i < urls.length; i++) {\n",
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              "    for (var i = 0; i < js_urls.length; i++) {\n",
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              "      element.onerror = on_error;\n",
              "      element.async = false;\n",
              "      element.src = url;\n",
              "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
              "      document.head.appendChild(element);\n",
              "    }\n",
              "    for (var i = 0; i < js_modules.length; i++) {\n",
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              "      element.async = false;\n",
              "      element.src = url;\n",
              "      element.type = \"module\";\n",
              "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
              "      document.head.appendChild(element);\n",
              "    }\n",
              "    if (!js_urls.length && !js_modules.length) {\n",
              "      on_load()\n",
              "    }\n",
              "  };\n",
              "\n",
              "  function inject_raw_css(css) {\n",
              "    const element = document.createElement(\"style\");\n",
              "    element.appendChild(document.createTextNode(css));\n",
              "    document.body.appendChild(element);\n",
              "  }\n",
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              "  var js_modules = [];\n",
              "  var css_urls = [\"https://unpkg.com/tabulator-tables@4.9.3/dist/css/tabulator_simple.min.css\", \"https://unpkg.com/@holoviz/panel@0.11.1/dist/css/loading.css\", \"https://unpkg.com/@holoviz/panel@0.11.1/dist/css/widgets.css\", \"https://unpkg.com/@holoviz/panel@0.11.1/dist/css/card.css\", \"https://unpkg.com/@holoviz/panel@0.11.1/dist/css/alerts.css\", \"https://unpkg.com/@holoviz/panel@0.11.1/dist/css/json.css\", \"https://unpkg.com/@holoviz/panel@0.11.1/dist/css/dataframe.css\", \"https://unpkg.com/@holoviz/panel@0.11.1/dist/css/markdown.css\"];\n",
              "  var inline_js = [\n",
              "    function(Bokeh) {\n",
              "      inject_raw_css(\"\\n    .bk.pn-loading.arcs:before {\\n      background-image: url(\\\"\\\")\\n    }\\n    \");\n",
              "    },\n",
              "    function(Bokeh) {\n",
              "      Bokeh.set_log_level(\"info\");\n",
              "    },\n",
              "    function(Bokeh) {} // ensure no trailing comma for IE\n",
              "  ];\n",
              "\n",
              "  function run_inline_js() {\n",
              "    if ((root.Bokeh !== undefined) || (force === true)) {\n",
              "      for (var i = 0; i < inline_js.length; i++) {\n",
              "        inline_js[i].call(root, root.Bokeh);\n",
              "      }} else if (Date.now() < root._bokeh_timeout) {\n",
              "      setTimeout(run_inline_js, 100);\n",
              "    } else if (!root._bokeh_failed_load) {\n",
              "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
              "      root._bokeh_failed_load = true;\n",
              "    }\n",
              "  }\n",
              "\n",
              "  if (root._bokeh_is_loading === 0) {\n",
              "    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
              "    run_inline_js();\n",
              "  } else {\n",
              "    load_libs(css_urls, js_urls, js_modules, function() {\n",
              "      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
              "      run_inline_js();\n",
              "    });\n",
              "  }\n",
              "}(window));"
            ],
            "application/vnd.holoviews_load.v0+json": "\n(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, js_modules, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n    if (js_modules == null) js_modules = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls.length === 0 && js_modules.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error() {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (var i = 0; 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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
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              "\n",
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              "      } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n",
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              "    JupyterCommManager.prototype.get_client_comm = function(plot_id, comm_id, msg_handler) {\n",
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              "      } else if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n",
              "        var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n",
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              "  delete PyViz.comms[\"hv-extension-comm\"];\n",
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              "  OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
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              "  } catch(err) {\n",
              "  }\n",
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document.createElement(\"div\");\n    bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n    var script_attrs = bk_div.children[0].attributes;\n    for (var i = 0; i < script_attrs.length; i++) {\n      toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n    }\n    // store reference to server id on output_area\n    output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n  }\n}\n\n/**\n * Handle when an output is cleared or removed\n */\nfunction handle_clear_output(event, handle) {\n  var id = handle.cell.output_area._hv_plot_id;\n  var server_id = handle.cell.output_area._bokeh_server_id;\n  if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n  var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n  if (server_id !== null) {\n    comm.send({event_type: 'server_delete', 'id': server_id});\n    return;\n  } else if (comm !== null) {\n    comm.send({event_type: 'delete', 'id': id});\n  }\n  delete PyViz.plot_index[id];\n  if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n    var doc = window.Bokeh.index[id].model.document\n    doc.clear();\n    const i = window.Bokeh.documents.indexOf(doc);\n    if (i > -1) {\n      window.Bokeh.documents.splice(i, 1);\n    }\n  }\n}\n\n/**\n * Handle kernel restart event\n */\nfunction handle_kernel_cleanup(event, handle) {\n  delete PyViz.comms[\"hv-extension-comm\"];\n  window.PyViz.plot_index = {}\n}\n\n/**\n * Handle update_display_data messages\n */\nfunction handle_update_output(event, handle) {\n  handle_clear_output(event, {cell: {output_area: handle.output_area}})\n  handle_add_output(event, handle)\n}\n\nfunction register_renderer(events, OutputArea) {\n  function append_mime(data, metadata, element) {\n    // create a DOM node to render to\n    var toinsert = this.create_output_subarea(\n    metadata,\n    CLASS_NAME,\n    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          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AET-aTq2_-7-"
      },
      "source": [
        "We need to download the datasets, and the models"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aFs0aBZDWUwv"
      },
      "source": [
        "MODEL = {\"MS_SVCONV_2cm_X2_3head_3dm.pt\": \"https://cloud.mines-paristech.fr/index.php/s/hRc6y2YIFtYsGAI/download\",\n",
        "         \"MS_SVCONV_4cm_X2_3head_eth.pt\": \"https://cloud.mines-paristech.fr/index.php/s/pUmGPtHUG2ASxlJ/download\"}\n",
        "DATA = {\"gazebo_winter_12.pcd\": \"https://cloud.mines-paristech.fr/index.php/s/zgO88hYFeogTj2s/download\",\n",
        "        \"gazebo_winter_11.pcd\": \"https://cloud.mines-paristech.fr/index.php/s/NpsabVL7bz5qFEe/download\",\n",
        "        \"kitchen_0.ply\": \"https://cloud.mines-paristech.fr/index.php/s/lArxiaV0DPo4bBU/download\",\n",
        "        \"kitchen_10.ply\": \"https://cloud.mines-paristech.fr/index.php/s/357BXcA2qcrw2Uy/download\"}\n",
        "\n",
        "def download(url, out, name):\n",
        "  \"\"\"\n",
        "  download a file and extract the zip file\n",
        "  \"\"\"\n",
        "  req = requests.get(url)\n",
        "  pathlib.Path(out).mkdir(exist_ok=True)\n",
        "  with open(osp.join(out, name), \"wb\") as archive:\n",
        "    archive.write(req.content)\n",
        "def extract(out, name):\n",
        "  with ZipFile(osp.join(out, name+\".zip\"), \"r\") as zip_obj:\n",
        "    zip_obj.extractall(osp.join(out, name))\n",
        "# Download Models and data for the demo\n",
        "download(MODEL[\"MS_SVCONV_2cm_X2_3head_3dm.pt\"], \"models\", \"MS_SVCONV_2cm_X2_3head_3dm.pt\")\n",
        "download(MODEL[\"MS_SVCONV_4cm_X2_3head_eth.pt\"], \"models\", \"MS_SVCONV_4cm_X2_3head_eth.pt\")\n",
        "download(DATA[\"gazebo_winter_12.pcd\"], \"data\", \"gazebo_winter_12.pcd\")\n",
        "download(DATA[\"gazebo_winter_11.pcd\"], \"data\", \"gazebo_winter_11.pcd\")\n",
        "download(DATA[\"kitchen_0.ply\"], \"data\", \"kitchen_0.ply\")\n",
        "download(DATA[\"kitchen_10.ply\"], \"data\", \"kitchen_10.ply\")\n",
        "\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GNxw7wNb8Edi"
      },
      "source": [
        "def read_pcd(path):\n",
        "  pcd = o3d.io.read_point_cloud(path)\n",
        "  data = Pair(pos=torch.from_numpy(np.asarray(pcd.points)).float(), batch=torch.zeros(len(pcd.points)).long())\n",
        "  return data"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2rpBdgsBAHuN"
      },
      "source": [
        "modify the variable `choice_data` and `choice_model` if you want to change the dataset and the model. \n",
        "For the transformation, we perform random rotation beforehand just to show that MS-SVConv is rotation invariant. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1r4jSZWiungd"
      },
      "source": [
        "#@title Data and Model\n",
        "#@markdown Please choose the data and the model you want\n",
        "choice_data = \"eth\"  #@param ['eth', '3dm']\n",
        "choice_model = \"eth\"  #@param ['eth', '3dm']\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "egHArbbq6ISY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "4cb5a21c-1c05-46ec-d0d8-e9e28a1f3857"
      },
      "source": [
        "\n",
        "# DATA\n",
        "pcd_path = {\n",
        "    \"eth\": [\"data/gazebo_winter_12.pcd\", \"data/gazebo_winter_11.pcd\"],\n",
        "    \"3dm\": [\"data/kitchen_0.ply\", \"data/kitchen_10.ply\"] \n",
        "}\n",
        "# Model\n",
        "pcd_model = {\n",
        "    \"eth\": \"models/MS_SVCONV_4cm_X2_3head_eth.pt\",\n",
        "    \"3dm\": \"models/MS_SVCONV_2cm_X2_3head_3dm.pt\"\n",
        "}\n",
        "\n",
        "# Data augmentation\n",
        "transfo_3dm = Compose([Random3AxisRotation(rot_x=180, rot_y=180, rot_z=180), GridSampling3D(size=0.02, quantize_coords=True, mode='last'), AddOnes(), AddFeatByKey(add_to_x=True, feat_name=\"ones\")])\n",
        "transfo_eth = Compose([Random3AxisRotation(rot_x=180, rot_y=180, rot_z=180), GridSampling3D(size=0.04, quantize_coords=True, mode='last'), AddOnes(), AddFeatByKey(add_to_x=True, feat_name=\"ones\")])\n",
        "pcd_DA = {\n",
        "    \"eth\": transfo_eth,\n",
        "    \"3dm\": transfo_3dm\n",
        "}\n",
        "\n",
        "model = PretainedRegistry.from_file(pcd_model[choice_model], mock_property={})\n",
        "\n",
        "data_s = pcd_DA[choice_model](read_pcd(pcd_path[choice_data][0]))\n",
        "data_t = pcd_DA[choice_model](read_pcd(pcd_path[choice_data][1]))\n",
        "\n",
        "\n",
        "#pl1 = pvqt.BackgroundPlotter()\n",
        "pl1 = pv.Plotter(notebook=True)\n",
        "pl1.add_points(data_s.pos.numpy(),color=[0.9, 0.7, 0.1])\n",
        "pl1.add_points(data_t.pos.numpy(),color=[0.1, 0.7, 0.9])\n",
        "\n",
        "pl1.enable_eye_dome_lighting()\n",
        "pl1.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "invalid syntax (<string>, line 1)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Error trying to create ss_transform, [{'transform': 'ComposeTransform', 'params': {'transform_options': [{'transform': 'RandomParamTransform', 'params': {'transform_name': 'FixedSphereDropout', 'transform_params': {'radius': {'min': 0.4, 'max': 1.5, 'type': 'float'}, 'name_ind': {'value': 'ind_sensors'}}}}, {'transform': 'CubeCrop', 'params': {'c': 5, 'grid_size_center': 0.02}}]}}, {'transform': 'ComposeTransform', 'params': {'transform_options': [{'transform': 'RandomParamTransform', 'params': {'transform_name': 'FixedSphereDropout', 'transform_params': {'radius': {'min': 0.4, 'max': 1.5, 'type': 'float'}, 'name_ind': {'value': 'ind_sensors'}}}}, {'transform': 'IrregularSampling', 'params': {'d_half': 3.5, 'p': 1, 'grid_size_center': 0.01}}]}}, {'transform': 'ComposeTransform', 'params': {'transform_options': [{'transform': 'RandomParamTransform', 'params': {'transform_name': 'FixedSphereDropout', 'transform_params': {'radius': {'min': 0.4, 'max': 1.5, 'type': 'float'}, 'name_ind': {'value': 'ind_sensors'}}}}, {'transform': 'CubeCrop', 'params': {'c': 5.5, 'grid_size_center': 0.02}}]}}]\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch_points3d/datasets/base_dataset.py\", line 129, in set_transform\n",
            "    transform = instantiate_transforms(getattr(dataset_opt, key_name))\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch_points3d/core/data_transform/__init__.py\", line 91, in instantiate_transforms\n",
            "    transforms.append(instantiate_transform(transform))\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch_points3d/core/data_transform/__init__.py\", line 68, in instantiate_transform\n",
            "    raise ValueError(\"Transform %s is nowhere to be found\" % tr_name)\n",
            "ValueError: Transform ComposeTransform is nowhere to be found\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=1024x768 at 0x7F9E12A40050>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(71.86088783621754, 80.43261940360036, 68.7249167668816),\n",
              " (-1.04705810546875, 7.5246734619140625, -4.1830291748046875),\n",
              " (0.0, 0.0, 1.0)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hbeevGTAU52_"
      },
      "source": [
        "def ransac(pos_s, pos_t, feat_s, feat_t, distance_threshold=0.1):\n",
        "  pcd_s = o3d.geometry.PointCloud()\n",
        "  pcd_s.points = o3d.utility.Vector3dVector(pos_s.numpy())\n",
        "  pcd_t = o3d.geometry.PointCloud()\n",
        "  pcd_t.points = o3d.utility.Vector3dVector(pos_t.numpy())\n",
        "\n",
        "  f_s = o3d.registration.Feature()\n",
        "  f_s.data = feat_s.T.numpy()\n",
        "  f_t = o3d.registration.Feature()\n",
        "  f_t.data = feat_t.T.numpy()\n",
        "  result = o3d.registration.registration_ransac_based_on_feature_matching(\n",
        "      pcd_s, pcd_t, f_s, f_t,\n",
        "      distance_threshold,\n",
        "      o3d.registration.TransformationEstimationPointToPoint(False), 4,\n",
        "      [o3d.registration.CorrespondenceCheckerBasedOnEdgeLength(0.9),\n",
        "       o3d.registration.CorrespondenceCheckerBasedOnDistance(distance_threshold)],\n",
        "       o3d.registration.RANSACConvergenceCriteria(4000000, 500))\n",
        "  return torch.from_numpy(result.transformation).float()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mo8dbgNFApNv"
      },
      "source": [
        "To registrate, we use RANSAC of Open3D, but it's possible to use other estimator (such as Fast Global Registration or TEASER)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xZddsisSVtKr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "abe591b2-250a-44c5-994e-dd933957da03"
      },
      "source": [
        "# Let us registrate !!\n",
        "\n",
        "# Compute the matches\n",
        "with torch.no_grad():\n",
        "  model.set_input(data_s, \"cuda\")\n",
        "  feat_s = model.forward()\n",
        "  model.set_input(data_t, \"cuda\")\n",
        "  feat_t = model.forward()\n",
        "# select random points and compute transformation using FGR\n",
        "rand_s = torch.randint(0, len(feat_s), (5000, ))\n",
        "rand_t = torch.randint(0, len(feat_t), (5000, ))\n",
        "matches = get_matches(feat_s[rand_s], feat_t[rand_t], sym=True)\n",
        "T_est = ransac(data_s.pos[rand_s], data_t.pos[rand_t], feat_s[rand_s], feat_t[rand_t])\n",
        "print(T_est)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor([[-0.9056, -0.4202,  0.0576, -0.0107],\n",
            "        [-0.0810,  0.3047,  0.9490,  0.0064],\n",
            "        [-0.4163,  0.8547, -0.3100, -0.0183],\n",
            "        [ 0.0000,  0.0000,  0.0000,  1.0000]])\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:18: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at  /pytorch/torch/csrc/utils/tensor_numpy.cpp:143.)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5dGG8AbpCzW6",
        "outputId": "6567fab8-aa57-40cc-f533-87d32e5b1b5b"
      },
      "source": [
        "pl2 = pv.Plotter(notebook=True)\n",
        "transformed = data_s.pos @ T_est[:3, :3].T + T_est[:3, 3]\n",
        "pl2.add_points(np.asarray(transformed.cpu().numpy()),color=[0.9, 0.7, 0.1])\n",
        "pl2.add_points(np.asarray(data_t.pos.cpu().numpy()),color=[0.1, 0.7, 0.9])\n",
        "pl2.enable_eye_dome_lighting()\n",
        "pl2.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=1024x768 at 0x7F9E1305E250>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(66.74803265649554, 71.96136626321551, 76.60099276620623),\n",
              " (-5.776760578155518, -0.5634269714355469, 4.076199531555176),\n",
              " (0.0, 0.0, 1.0)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rd5Yv9K-A__Y"
      },
      "source": [
        "That's it ! Now, you know how to use MS-SVConv ! Don't hesitate using it in you project. And if you like it, please cite our work :\n",
        "```\n",
        "@inproceedings{horache2021mssvconv,\n",
        "      title={3D Point Cloud Registration with Multi-Scale Architecture and Self-supervised Fine-tuning}, \n",
        "      author={Sofiane Horache and Jean-Emmanuel Deschaud and François Goulette},\n",
        "      year={2021},\n",
        "      journal={arXiv preprint arXiv:2103.14533}\n",
        "}\n",
        "```"
      ]
    }
  ]
}